Machine translation: a view from the Lexicon
Machine translation: a view from the Lexicon
Machine translation divergences: a formal description and proposed solution
Computational Linguistics
Rapid Prototyping of Domain-Specific Machine Translation Systems
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
A framework for MT and multilingual NLG systems based on uniform lexico-structural processing
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A fast and portable realizer for text generation systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Using lexicalized tags for machine translation
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignment
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
Learning domain-specific transfer rules: an experiment with Korean to English translation
COLING-MTIA '02 Proceedings of the 2002 COLING workshop on Machine translation in Asia - Volume 16
Statistical dependency parsing in Korean: from corpus generation to automatic parsing
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
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This paper describes an approach for handling structural divergences and recovering dropped arguments in an implemented Korean to English machine translation system. The approach relies on canonical predicate-argument structures (or dependency structures), which provide a suitable pivot representation for the handling of structural divergences and the recovery of dropped arguments. It can also be converted to and from the interface representations of many off-the-shelf parsers and generators.